Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells12
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Text3
Categorical1
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15819/S/1/datasetView.do

Alerts

금액 is highly skewed (γ1 = 59.78131168)Skewed

Reproduction

Analysis started2024-05-11 02:36:19.057080
Analysis finished2024-05-11 02:36:22.820841
Duration3.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

Distinct2128
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:36:23.117120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length7.2757
Min length2

Characters and Unicode

Total characters72757
Distinct characters431
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)2.1%

Sample

1st row가락삼익맨션
2nd row신내새한아파트
3rd row양평거성파스텔
4th row신내건영2차아파트
5th row신반포 한신 25,26,27차 아파트
ValueCountFrequency (%)
아파트 124
 
1.2%
래미안 41
 
0.4%
입주자대표회의 26
 
0.2%
아이파크 26
 
0.2%
고덕 22
 
0.2%
올림픽선수기자촌아파트 21
 
0.2%
잠실파크리오 20
 
0.2%
벽산라이브파크 19
 
0.2%
마포래미안푸르지오 19
 
0.2%
헬리오시티아파트 18
 
0.2%
Other values (2189) 10306
96.8%
2024-05-11T02:36:24.066615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2368
 
3.3%
파 2339
 
3.2%
트 2126
 
2.9%
지 2013
 
2.8%
대 1841
 
2.5%
단 1620
 
2.2%
동 1535
 
2.1%
신 1459
 
2.0%
차 1393
 
1.9%
이 1290
 
1.8%
Other values (421) 54773
75.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66414
91.3%
Decimal Number 3880
 
5.3%
Uppercase Letter 888
 
1.2%
Space Separator 706
 
1.0%
Lowercase Letter 297
 
0.4%
Open Punctuation 149
 
0.2%
Close Punctuation 149
 
0.2%
Other Punctuation 140
 
0.2%
Dash Punctuation 124
 
0.2%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2368
 
3.6%
파 2339
 
3.5%
트 2126
 
3.2%
지 2013
 
3.0%
대 1841
 
2.8%
단 1620
 
2.4%
동 1535
 
2.3%
신 1459
 
2.2%
차 1393
 
2.1%
이 1290
 
1.9%
Other values (375) 48430
72.9%
Uppercase Letter
ValueCountFrequency (%)
S 173
19.5%
K 120
13.5%
C 100
11.3%
M 70
7.9%
D 70
7.9%
E 55
 
6.2%
L 48
 
5.4%
I 44
 
5.0%
H 43
 
4.8%
A 41
 
4.6%
Other values (7) 124
14.0%
Lowercase Letter
ValueCountFrequency (%)
e 193
65.0%
l 26
 
8.8%
i 24
 
8.1%
v 13
 
4.4%
s 9
 
3.0%
k 9
 
3.0%
g 6
 
2.0%
a 6
 
2.0%
w 5
 
1.7%
c 4
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 1173
30.2%
2 1159
29.9%
3 494
12.7%
4 248
 
6.4%
5 225
 
5.8%
6 187
 
4.8%
7 125
 
3.2%
9 104
 
2.7%
8 86
 
2.2%
0 79
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 113
80.7%
. 27
 
19.3%
Space Separator
ValueCountFrequency (%)
706
100.0%
Open Punctuation
ValueCountFrequency (%)
( 149
100.0%
Close Punctuation
ValueCountFrequency (%)
) 149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66414
91.3%
Common 5154
 
7.1%
Latin 1189
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2368
 
3.6%
파 2339
 
3.5%
트 2126
 
3.2%
지 2013
 
3.0%
대 1841
 
2.8%
단 1620
 
2.4%
동 1535
 
2.3%
신 1459
 
2.2%
차 1393
 
2.1%
이 1290
 
1.9%
Other values (375) 48430
72.9%
Latin
ValueCountFrequency (%)
e 193
16.2%
S 173
14.6%
K 120
10.1%
C 100
 
8.4%
M 70
 
5.9%
D 70
 
5.9%
E 55
 
4.6%
L 48
 
4.0%
I 44
 
3.7%
H 43
 
3.6%
Other values (19) 273
23.0%
Common
ValueCountFrequency (%)
1 1173
22.8%
2 1159
22.5%
706
13.7%
3 494
9.6%
4 248
 
4.8%
5 225
 
4.4%
6 187
 
3.6%
( 149
 
2.9%
) 149
 
2.9%
7 125
 
2.4%
Other values (7) 539
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66414
91.3%
ASCII 6339
 
8.7%
Number Forms 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2368
 
3.6%
파 2339
 
3.5%
트 2126
 
3.2%
지 2013
 
3.0%
대 1841
 
2.8%
단 1620
 
2.4%
동 1535
 
2.3%
신 1459
 
2.2%
차 1393
 
2.1%
이 1290
 
1.9%
Other values (375) 48430
72.9%
ASCII
ValueCountFrequency (%)
1 1173
18.5%
2 1159
18.3%
706
11.1%
3 494
 
7.8%
4 248
 
3.9%
5 225
 
3.5%
e 193
 
3.0%
6 187
 
2.9%
S 173
 
2.7%
( 149
 
2.4%
Other values (35) 1632
25.7%
Number Forms
ValueCountFrequency (%)
Ⅰ 4
100.0%

아파트코드
Text

Distinct2135
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:36:24.785966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)2.1%

Sample

1st rowA13885306
2nd rowA13187406
3rd rowA15010306
4th rowA13185607
5th rowA13790716
ValueCountFrequency (%)
a13805002 21
 
0.2%
a13824006 20
 
0.2%
a14272305 19
 
0.2%
a12175203 19
 
0.2%
a10025850 18
 
0.2%
a13822004 16
 
0.2%
a14272314 16
 
0.2%
a13485302 16
 
0.2%
a13583507 15
 
0.1%
a15209207 15
 
0.1%
Other values (2125) 9825
98.2%
2024-05-11T02:36:25.907348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18544
20.6%
1 17211
19.1%
A 9990
11.1%
3 8984
10.0%
2 8086
9.0%
5 6370
 
7.1%
8 5647
 
6.3%
7 5049
 
5.6%
4 3751
 
4.2%
6 3360
 
3.7%
Other values (2) 3008
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
88.9%
Uppercase Letter 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18544
23.2%
1 17211
21.5%
3 8984
11.2%
2 8086
10.1%
5 6370
 
8.0%
8 5647
 
7.1%
7 5049
 
6.3%
4 3751
 
4.7%
6 3360
 
4.2%
9 2998
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 9990
99.9%
B 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 80000
88.9%
Latin 10000
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18544
23.2%
1 17211
21.5%
3 8984
11.2%
2 8086
10.1%
5 6370
 
8.0%
8 5647
 
7.1%
7 5049
 
6.3%
4 3751
 
4.7%
6 3360
 
4.2%
9 2998
 
3.7%
Latin
ValueCountFrequency (%)
A 9990
99.9%
B 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18544
20.6%
1 17211
19.1%
A 9990
11.1%
3 8984
10.0%
2 8086
9.0%
5 6370
 
7.1%
8 5647
 
6.3%
7 5049
 
5.6%
4 3751
 
4.2%
6 3360
 
3.7%
Other values (2) 3008
 
3.3%

비용명
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
연체료수익
3871 
승강기수익
1130 
잡수익
902 
주차장수익
839 
광고료수익
679 
Other values (10)
2579 

Length

Max length9
Median length5
Mean length4.9032
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연체료수익
2nd row연체료수익
3rd row승강기수익
4th row재활용품수익
5th row연체료수익

Common Values

ValueCountFrequency (%)
연체료수익 3871
38.7%
승강기수익 1130
 
11.3%
잡수익 902
 
9.0%
주차장수익 839
 
8.4%
광고료수익 679
 
6.8%
이자수익 640
 
6.4%
기타운영수익 470
 
4.7%
고용안정사업수익 295
 
2.9%
검침수익 291
 
2.9%
알뜰시장수익 242
 
2.4%
Other values (5) 641
 
6.4%

Length

2024-05-11T02:36:26.353429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연체료수익 3871
38.7%
승강기수익 1130
 
11.3%
잡수익 902
 
9.0%
주차장수익 839
 
8.4%
광고료수익 679
 
6.8%
이자수익 640
 
6.4%
기타운영수익 470
 
4.7%
고용안정사업수익 295
 
2.9%
검침수익 291
 
2.9%
알뜰시장수익 242
 
2.4%
Other values (5) 641
 
6.4%

년월일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200317
Minimum20200301
Maximum20200331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:36:26.920043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200301
5-th percentile20200302
Q120200309
median20200318
Q320200326
95-th percentile20200331
Maximum20200331
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.8426263
Coefficient of variation (CV)4.8725108 × 10-7
Kurtosis-1.3162516
Mean20200317
Median Absolute Deviation (MAD)8
Skewness-0.15412636
Sum2.0200317 × 1011
Variance96.877293
MonotonicityNot monotonic
2024-05-11T02:36:27.474493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20200331 1012
 
10.1%
20200302 610
 
6.1%
20200330 565
 
5.7%
20200325 459
 
4.6%
20200310 419
 
4.2%
20200324 394
 
3.9%
20200303 381
 
3.8%
20200321 364
 
3.6%
20200326 361
 
3.6%
20200327 360
 
3.6%
Other values (21) 5075
50.7%
ValueCountFrequency (%)
20200301 243
 
2.4%
20200302 610
6.1%
20200303 381
3.8%
20200304 337
3.4%
20200305 338
3.4%
20200306 297
3.0%
20200307 80
 
0.8%
20200308 61
 
0.6%
20200309 302
3.0%
20200310 419
4.2%
ValueCountFrequency (%)
20200331 1012
10.1%
20200330 565
5.7%
20200329 184
 
1.8%
20200328 144
 
1.4%
20200327 360
 
3.6%
20200326 361
 
3.6%
20200325 459
4.6%
20200324 394
 
3.9%
20200323 327
 
3.3%
20200322 115
 
1.1%

금액
Real number (ℝ)

SKEWED 

Distinct3729
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264598.48
Minimum-2600000
Maximum2.2824 × 108
Zeros9
Zeros (%)0.1%
Negative57
Negative (%)0.6%
Memory size166.0 KiB
2024-05-11T02:36:27.995641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2600000
5-th percentile170
Q12679.75
median23339.5
Q3100000
95-th percentile940200
Maximum2.2824 × 108
Range2.3084 × 108
Interquartile range (IQR)97320.25

Descriptive statistics

Standard deviation3016990.9
Coefficient of variation (CV)11.402147
Kurtosis4103.773
Mean264598.48
Median Absolute Deviation (MAD)22822.5
Skewness59.781312
Sum2.6459848 × 109
Variance9.102234 × 1012
MonotonicityNot monotonic
2024-05-11T02:36:28.470697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 479
 
4.8%
30000 470
 
4.7%
50000 453
 
4.5%
150000 154
 
1.5%
70000 153
 
1.5%
40000 129
 
1.3%
200000 128
 
1.3%
60000 120
 
1.2%
20000 107
 
1.1%
80000 86
 
0.9%
Other values (3719) 7721
77.2%
ValueCountFrequency (%)
-2600000 1
< 0.1%
-2145000 1
< 0.1%
-1800000 1
< 0.1%
-1500000 1
< 0.1%
-1405800 1
< 0.1%
-1281600 1
< 0.1%
-1234200 1
< 0.1%
-640000 1
< 0.1%
-540000 1
< 0.1%
-431200 1
< 0.1%
ValueCountFrequency (%)
228240000 1
< 0.1%
161832937 1
< 0.1%
63742459 1
< 0.1%
20232000 1
< 0.1%
20000000 1
< 0.1%
19788700 1
< 0.1%
18181818 1
< 0.1%
17371057 1
< 0.1%
16217800 1
< 0.1%
16200000 1
< 0.1%

내용
Text

Distinct5491
Distinct (%)55.0%
Missing12
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T02:36:29.378524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length63
Mean length13.626452
Min length1

Characters and Unicode

Total characters136101
Distinct characters707
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5259 ?
Unique (%)52.7%

Sample

1st row관리비 연체료 수납
2nd row관리비 연체료 수납
3rd row이사(102-1202호)
4th row재활용수거비 - 한국재생자원
5th row관리비 연체료 수납
ValueCountFrequency (%)
관리비 4005
 
15.2%
연체료 3882
 
14.8%
수납 3877
 
14.7%
3월분 314
 
1.2%
승강기 307
 
1.2%
승강기사용료 287
 
1.1%
225
 
0.9%
입금 214
 
0.8%
2월분 206
 
0.8%
사용료 198
 
0.8%
Other values (6852) 12777
48.6%
2024-05-11T02:36:31.222134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16418
 
12.1%
료 5822
 
4.3%
수 5290
 
3.9%
리 5104
 
3.8%
0 4884
 
3.6%
비 4851
 
3.6%
관 4318
 
3.2%
1 4230
 
3.1%
체 4033
 
3.0%
납 3943
 
2.9%
Other values (697) 77208
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88170
64.8%
Decimal Number 20062
 
14.7%
Space Separator 16418
 
12.1%
Close Punctuation 2805
 
2.1%
Open Punctuation 2803
 
2.1%
Other Punctuation 2414
 
1.8%
Dash Punctuation 2265
 
1.7%
Uppercase Letter 697
 
0.5%
Math Symbol 288
 
0.2%
Lowercase Letter 106
 
0.1%
Other values (3) 73
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 5822
 
6.6%
수 5290
 
6.0%
리 5104
 
5.8%
비 4851
 
5.5%
관 4318
 
4.9%
체 4033
 
4.6%
납 3943
 
4.5%
연 3934
 
4.5%
기 1709
 
1.9%
월 1694
 
1.9%
Other values (615) 47472
53.8%
Uppercase Letter
ValueCountFrequency (%)
N 72
 
10.3%
K 58
 
8.3%
T 48
 
6.9%
A 47
 
6.7%
C 46
 
6.6%
B 44
 
6.3%
L 42
 
6.0%
O 40
 
5.7%
S 40
 
5.7%
D 35
 
5.0%
Other values (15) 225
32.3%
Other Punctuation
ValueCountFrequency (%)
/ 710
29.4%
. 687
28.5%
, 663
27.5%
: 163
 
6.8%
* 108
 
4.5%
@ 28
 
1.2%
% 15
 
0.6%
# 14
 
0.6%
' 7
 
0.3%
? 6
 
0.2%
Other values (6) 13
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
o 40
37.7%
x 13
 
12.3%
k 11
 
10.4%
n 10
 
9.4%
b 8
 
7.5%
c 7
 
6.6%
s 4
 
3.8%
e 4
 
3.8%
t 3
 
2.8%
a 2
 
1.9%
Other values (4) 4
 
3.8%
Decimal Number
ValueCountFrequency (%)
0 4884
24.3%
1 4230
21.1%
2 3335
16.6%
3 2582
12.9%
4 1202
 
6.0%
5 1035
 
5.2%
6 832
 
4.1%
9 669
 
3.3%
8 648
 
3.2%
7 645
 
3.2%
Math Symbol
ValueCountFrequency (%)
~ 232
80.6%
> 17
 
5.9%
+ 17
 
5.9%
< 9
 
3.1%
× 7
 
2.4%
= 4
 
1.4%
÷ 2
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 2739
97.7%
[ 63
 
2.2%
{ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 2741
97.7%
] 64
 
2.3%
Space Separator
ValueCountFrequency (%)
16418
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2265
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 70
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Number
ValueCountFrequency (%)
③ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88166
64.8%
Common 47128
34.6%
Latin 803
 
0.6%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 5822
 
6.6%
수 5290
 
6.0%
리 5104
 
5.8%
비 4851
 
5.5%
관 4318
 
4.9%
체 4033
 
4.6%
납 3943
 
4.5%
연 3934
 
4.5%
기 1709
 
1.9%
월 1694
 
1.9%
Other values (611) 47468
53.8%
Common
ValueCountFrequency (%)
16418
34.8%
0 4884
 
10.4%
1 4230
 
9.0%
2 3335
 
7.1%
) 2741
 
5.8%
( 2739
 
5.8%
3 2582
 
5.5%
- 2265
 
4.8%
4 1202
 
2.6%
5 1035
 
2.2%
Other values (33) 5697
 
12.1%
Latin
ValueCountFrequency (%)
N 72
 
9.0%
K 58
 
7.2%
T 48
 
6.0%
A 47
 
5.9%
C 46
 
5.7%
B 44
 
5.5%
L 42
 
5.2%
o 40
 
5.0%
O 40
 
5.0%
S 40
 
5.0%
Other values (29) 326
40.6%
Han
ValueCountFrequency (%)
入 1
25.0%
不 1
25.0%
人 1
25.0%
內 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88165
64.8%
ASCII 47918
35.2%
None 12
 
< 0.1%
CJK 3
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16418
34.3%
0 4884
 
10.2%
1 4230
 
8.8%
2 3335
 
7.0%
) 2741
 
5.7%
( 2739
 
5.7%
3 2582
 
5.4%
- 2265
 
4.7%
4 1202
 
2.5%
5 1035
 
2.2%
Other values (66) 6487
 
13.5%
Hangul
ValueCountFrequency (%)
료 5822
 
6.6%
수 5290
 
6.0%
리 5104
 
5.8%
비 4851
 
5.5%
관 4318
 
4.9%
체 4033
 
4.6%
납 3943
 
4.5%
연 3934
 
4.5%
기 1709
 
1.9%
월 1694
 
1.9%
Other values (610) 47467
53.8%
None
ValueCountFrequency (%)
× 7
58.3%
÷ 2
 
16.7%
· 1
 
8.3%
＠ 1
 
8.3%
？ 1
 
8.3%
CJK
ValueCountFrequency (%)
入 1
33.3%
人 1
33.3%
內 1
33.3%
CJK Compat Ideographs
ValueCountFrequency (%)
不 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
③ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
100.0%

Interactions

2024-05-11T02:36:21.589847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:36:21.035218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:36:21.927440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:36:21.311630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:36:31.511932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비용명년월일금액
비용명1.0000.4600.057
년월일0.4601.0000.025
금액0.0570.0251.000
2024-05-11T02:36:31.770689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일금액비용명
년월일1.0000.0270.191
금액0.0271.0000.032
비용명0.1910.0321.000

Missing values

2024-05-11T02:36:22.285929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:36:22.679503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

아파트명아파트코드비용명년월일금액내용
37391가락삼익맨션A13885306연체료수익202003116030관리비 연체료 수납
17564신내새한아파트A13187406연체료수익202003134660관리비 연체료 수납
48505양평거성파스텔A15010306승강기수익20200324100000이사(102-1202호)
17016신내건영2차아파트A13185607재활용품수익20200306505910재활용수거비 - 한국재생자원
34246신반포 한신 25,26,27차 아파트A13790716연체료수익202003115390관리비 연체료 수납
46957여의도장미A15001004잡수익2020033020000신광세탁(03월분)
55542남서울럭키아파트A15386506연체료수익20200330520관리비 연체료 수납
22250서울숲푸르지오A13380803연체료수익202003041350관리비 연체료 수납
58253강서힐스테이트아파트A15701007연체료수익2020030111260관리비 연체료 수납
34049우면대림A13790004승강기수익2020030350000승강기사용료-102동 703호
아파트명아파트코드비용명년월일금액내용
10397신촌포스빌A12172201임대료수익20200311454545시설임대수입-20.04월
50107신대림신동아파밀리에A15095002연체료수익202003095400관리비 연체료 수납
9390마포쌍용황금A12105001연체료수익202003284910관리비 연체료 수납
62482신정현대A15807204연체료수익20200303920관리비 연체료 수납
21489옥수삼성임대A13375905연체료수익202003311400관리비 연체료 수납
42315중계무지개아파트A13986504연체료수익202003021245970관리비 연체료 수납
52177고척양우A15208201연체료수익202003054550관리비 연체료 수납
21528래미안옥수리버젠A13375907연체료수익202003095330관리비 연체료 수납
56598래미안트윈파크A15606007승강기수익20200331100000103-2403호 승강기 사용료 (전입)
35544문정시영A13820007연체료수익2020032712600관리비 연체료 수납